The problem is actually different. Race is often very predictive. Blacks don't perform worse simply because they have income < $10k. Blacks perform worse on ma

The problem is actually different. Race is often very predictive. Blacks don't perform worse simply because they have income < $10k. Blacks perform worse on many measures even if you hold all that stuff equal. Here are a few of the many studies reproducing this: http://ftp.iza.org/dp8733.pdf http://ftp.iza.org/dp8733.pdf http://www.mindingthecampus.org/2010/09/the_underperformance_problem/ http://www.mindingthecampus.org/2010/09/the_underperformance... https://randomcriticalanalysis.wordpress.com/2015/05/16/on-concentrated-poverty-and-its-effects-on-academic-outcomes/ https://randomcriticalanalysis.wordpress.com/2015/05/16/on-c... https://randomcriticalanalysis.wordpress.com/2015/11/22/on-the-relationship-between-negative-home-owner-equity-and-racial-demographics/#my_analysis https://randomcriticalanalysis.wordpress.com/2015/11/22/on-t... If you were right, then the machine learning algorithm would quickly determine that race doesn't matter. Redundant encoding, direct encoding, etc would be irrelevant - the algorithm would ignore it as noise. The problem is that the algorithm is supposed to uncover hidden patterns that predict loan default. But then there is one specific hidden pattern that is socially and legally taboo to detect, yet also highly predictive. (Incidentally, if you can solve this problem in lending, it's easily a unicorn startup.)